Fuzz testing (fuzzing) has witnessed its prosperity in detecting security flaws recently. It
generates a large number of test cases and monitors the executions for defects. Fuzzing has …

Fuzzing is a powerful tool for vulnerability discovery in software, with much progress being
made in the field in recent years. There is limited literature available on the fuzzing …

Fuzzing has proven to be a highly effective approach to uncover software bugs over the past
decade. After AFL popularized the groundbreaking concept of lightweight coverage …

Deep learning (DL) systems can make our life much easier, and thus are gaining more and
more attention from both academia and industry. Meanwhile, bugs in DL systems can be …

Seed scheduling, the order in which seeds are selected, can greatly affect the performance
of a fuzzer. Existing approaches schedule seeds based on their historical mutation data, but …

Deep Learning (DL) has gained wide attention in recent years. Meanwhile, bugs in DL
systems can lead to serious consequences, and may even threaten human lives. As a result …

Grey-box fuzz testing has revealed thousands of vulnerabilities in real-world software owing
to its lightweight instrumentation, fast coverage feedback, and dynamic adjusting strategies …

Coverage-guided fuzz testing (" fuzzing") has become mainstream and we have observed
lots of progress in this research area recently. However, it is still challenging to efficiently test …

Static analysis tools for capturing bugs and vulnerabilities in software programs are widely
employed in practice, as they have the unique advantages of high coverage and …

Existing coverage-based fuzzers usually use the individual control flow graph (CFG) edge
coverage to guide the fuzzing process, which has shown great potential in finding …